Abstract
This paper presents a method for re-weighting the frame-based scores of a speaker recognition system according to the discrimination level of the best matched Gaussian mixture for that frame. This approach focuses on particular feature space regions that either have been modeled accurately or contain the phonemes which are inherently most discriminative.